SciBERT-based Semantification of Bioassays in the Open Research Knowledge Graph
dc.bibliographicCitation.bookTitle | Proceedings of the EKAW 2020 Posters and Demonstrations Session co-located with 22nd International Conference on Knowledge Engineering and Knowledge Management (EKAW 2020) | eng |
dc.bibliographicCitation.firstPage | 22 | eng |
dc.bibliographicCitation.journalTitle | CEUR Workshop Proceedings | eng |
dc.bibliographicCitation.lastPage | 30 | eng |
dc.contributor.author | Anteghini, Marco | |
dc.contributor.author | D'Souza, Jennifer | |
dc.contributor.author | Martins dos Santos, Vitor A.P. | |
dc.contributor.author | Auer, Sören | |
dc.date.accessioned | 2021-04-13T08:21:41Z | |
dc.date.available | 2021-04-13T08:21:41Z | |
dc.date.issued | 2020 | |
dc.description.abstract | As a novel contribution to the problem of semantifying bio- logical assays, in this paper, we propose a neural-network-based approach to automatically semantify, thereby structure, unstructured bioassay text descriptions. Experimental evaluations, to this end, show promise as the neural-based semantification significantly outperforms a naive frequencybased baseline approach. Specifically, the neural method attains 72% F1 versus 47% F1 from the frequency-based method. The work in this paper aligns with the present cutting-edge trend of the scholarly knowledge digitalization impetus which aim to convert the long-standing document-based format of scholarly content into knowledge graphs (KG). To this end, our selected data domain of bioassays are a prime candidate for structuring into KGs. | eng |
dc.identifier.uri | https://oa.tib.eu/renate/handle/123456789/6144 | |
dc.identifier.uri | https://doi.org/10.34657/5192 | |
dc.language.iso | eng | eng |
dc.publisher | Aachen : RWTH | eng |
dc.relation.essn | 1613-0073 | |
dc.rights.license | CC BY 4.0 Unported | eng |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | eng |
dc.subject.ddc | 004 | eng |
dc.subject.gnd | Konferenzschrift | ger |
dc.subject.other | Open Science Graphs | eng |
dc.subject.other | Bioassays | eng |
dc.subject.other | Machine Learning | eng |
dc.title | SciBERT-based Semantification of Bioassays in the Open Research Knowledge Graph | eng |
dc.type | BookPart | eng |
dc.type | Text | eng |
dcterms.event | 22nd International Conference on Knowledge Engineering and Knowledge Management (EKAW 2020), 17. September 2020, online | |
tib.accessRights | openAccess | eng |
wgl.contributor | TIB | eng |
wgl.subject | Informatik | eng |
wgl.type | Buchkapitel / Sammelwerksbeitrag | eng |
wgl.type | Konferenzbeitrag | eng |
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